Abstract
Many Applications perceive visual information through networks of embedded sensors. Intensive image processing computations have to be performed in order to process the perceived information. Such computations usually demand hardware implementations in order to exhibit real time performance. Furthermore, many of such applications are hard to be characterized a priori, since they take different paths according to events happening in the scene at runtime. Hence, reconfigurable hardware devices are the only viable platform for implementing such applications, providing both real time performance and dynamic adaptability for the system.
In this paper, we present a collaborative and dynamically adaptive object tracking system that has been built in our lab. We exploit reconfigurable hardware devices embedded in a number of networked cameras in order to achieve our goal. We justify the need for dynamic adaptation of the system through scenarios and applications. Experimental results on a set of scenes advocate the fact that our system works effectively for different scenario of events through reconfiguration. Comparing results with non-adaptive implementations verify the fact that our approach improves system's robustness to scene variations and outperforms the traditional implementations.
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References
H. C. Andrews and B. R. Hunt. Digital Image Restoration. Prentice Hall, 1977.
P. Athanas and L. Abbott. Addressing the computational requirements of image processing with a custom computing machine: an overview. In Proceedings of the 2nd Workshop on Reconfigurable Architectures, April 1995, Santa Barbara, CA.
A. Benedetti and P. Perona. Real-time 2-D feature detection on a reconfigurable computer. IEEE Conference on Computer Vision and Pattern Recognition, June 1998, Santa Barbara, CA.
J. Biemond, J. Rieske, and J. J. Gerbrands. A fast kalman filter for images degraded by both blur and noise. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1983.
G. Bilardi and M. Sarrafzadeh. Optimal VLSI circuits for discrete fourier transform. Advances in Computing Research, 4:87–101, 1987.
F. Cuzzolin, A. Bissacco, R. Frezza, and S. Soatto. Towards unsupervised detection of actions in clutter. Proc. of the Asilomar Conference on Signals, Systems and Computers, 2002.
D. Estrin et al. Embedded, everywhere: a research agenda for networked systems of embedded computers. Committee on Networked Systems of Embedded Computers, Computer Science and Telecommunications Board, National Research Council, Washington, DC, 2001.
X. Feng and P. Perona. Real time motion detection system and scene segmentation. CDS TR CDS98-004, Caltech, 1998.
B. Fortner, T. E. Meyer, and T. Meyer. Number by Colors: A Guide to Using Color to Understand Technical Data. Springer Verlag, 1997.
S. Ghiasi, H. J. Moon, and M. Sarrafzadeh. Collaborative and reconfigurable object tracking. Engineering of Reconfigurable Systems and Algorithms, 2003.
IQinVision Online Documentations, IQinVision Inc., http://www.iqinvision.com.
D. J. Li, L. Jiang, T. Isshiki, and H. Kunieda. New VLSI array processor design for image window operations. IEEE Transactions on Circuits and Systems, 46(5):635–640, 1999.
A. K. Katsaggelos. Iterative image restoration algorithms. Optical Eng., 28:735–748, 1989.
B. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. International Joint Conference on Artificial Intelligence, 674–679, 1981.
M. Maire, Design and implementation of a realtime visual feature tracking system on a programmable video camera. Technical Report, California Institute of Technology, 2002.
K. Melhorn and F. Preparata. Area-time optimal vlsi integer multiplier with minimum computation time. Information and Control, 58:137–156, 1983.
ModelSim product manual, Model Technology Inc., http://www.model.com.
S. Ogrenci Memik, A. K. Katsaggelos, and M. Sarrafzadeh. FPGA implementation and analysis of an iterative image restoration algorithm. IEEE Transactions on Computers, 52(3), 2003.
J. C. Russ. the image processing handbook. CRC Press, 1999.
M. Sarrafzadeh, A. K. Katsaggelos, and S. P. Kumar. In Parallel architectures for iterative image restoration. Kluwer Academic, M. Bayoumi editor, 1991.
J. Shi and C. Tomasi. Good features to track. IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600, 1994.
Synplify Pro product manual, Synplicity Inc., http://www.sinplicity.com.
D. Tennenhouse. Proactive computing. Communications of the ACm, 43(5):59–66, 2000.
C. Tomasi and T. Kanade. Detection and tracking of point features. Carnegie Mellon University Technical Report CMU-CS-91-132, April 1991.
H. J. Trussel and B. R. Hunt. Improved methods of maximum a posteriori restoration. IEEE Transactions On Computers, 28, 1979.
M. Weiser. The computer for the 21st century. Scientific American, 265(3):94–104, 1991.
Xilinx Online Documentations, Xilinx Inc., http://www.xilinx.com.
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Ghiasi, S., Moon, H.J., Nahapetian, A. et al. Collaborative and Reconfigurable Object Tracking. The Journal of Supercomputing 30, 213–238 (2004). https://doi.org/10.1023/B:SUPE.0000045210.48347.ee
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DOI: https://doi.org/10.1023/B:SUPE.0000045210.48347.ee